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Measurement and Forecasting of High-Speed Rail Track Slab Deformation under Uncertain SHM Data Using Variational

Qi-Ang Wang1, Yi-Qing Ni2

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Summary

This study introduces a new system for monitoring high-speed rail track deformation, improving accuracy by accounting for varied sensor uncertainties. The Variational Heteroscedastic Gaussian Process model enhances data modeling and forecasting for structural health monitoring.

Keywords:
Heteroscedastic Gaussian Processfiber Bragg gratinghigh-speed railmeasurement and forecastingstructural health monitoringuncertainty

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Data Science

Background:

  • Accurate measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation is crucial for infrastructure safety.
  • Standard Gaussian Process (GP) models assume uniform noise, which is inadequate for HSR data due to heteroscedastic uncertainty from dynamic loads, environmental factors, and maintenance.
  • Existing methods struggle to accurately model and predict track slab deformation under complex, real-world conditions.

Purpose of the Study:

  • To develop an online structural health monitoring (SHM) system for continuous HSR track slab deformation monitoring.
  • To address and model the heteroscedastic uncertainty inherent in HSR SHM data.
  • To enhance the accuracy of regression and forecasting models for HSR track slab deformation.

Main Methods:

  • Development of a novel online SHM system using fiber Bragg grating (FBG) technology for electromagnetic interference elimination and temperature self-compensation.
  • Application of the Variational Heteroscedastic Gaussian Process (VHGP) approach, incorporating variational Bayesian and Gaussian approximation.
  • Utilizing in-situ measurement data for model training, uncertainty estimation, and deformation profile analysis.

Main Results:

  • The VHGP framework provides more robust regression results compared to standard methods.
  • The estimated confidence levels accurately reflect the heteroscedastic variances in HSR data.
  • VHGP achieves higher accuracy in both regression and forecasting, with improved prediction of maximum noise positions and smooth confidence intervals.
  • Identification and summarization of three typical uncertainty types during the M&F process.

Conclusions:

  • The developed online SHM system with FBG technology effectively monitors HSR track slab deformation.
  • The VHGP approach is superior for modeling and forecasting HSR track slab deformation due to its ability to handle heteroscedastic uncertainty.
  • The findings provide a more reliable method for assessing the structural health and predicting the future behavior of HSR infrastructure.